https://www.deviantart.com/benson-fangirl31/art/Where-Do-Babies-Come-From-392463543
6/16/2021
1 Month before Due Date:
Lockdown
Stages of labor and birth:
Baby, it’s time!
/0. Prodromal labor
- Early labor and active labor
- The birth of your baby
- Delivery of the placenta
Prodromal labor
Prodromal labor is labor that starts and stops before fully active labor begins. It’s often called “false labor,” but this is a poor description.
Early labor
You’ll feel mild, irregular contractions.
How long it lasts: Early labor is unpredictable. For first-time moms, the average length varies from hours to days. It’s often shorter for subsequent deliveries.
Active labor
… You might feel your water break — if it hasn’t already… If you haven’t headed to your labor and delivery facility yet, now’s the time.
How long it lasts: Active labor often lasts four to eight hours or more.
3-1-1
Traditionally the 5-1-1 rule is used; that is, when contractions come every 5 minutes, each lasting a full minute, and have been that way for an hour. More recent recommendations are 4-1-1 (four minutes apart) or even 3-1-1 (three minutes apart).
Monday - 20/04/06:
13 Days Before Due Date
“It’s prodromal labor. Don’t worry about it.”
Wednesday - 20/04/08:
11 Days Before Due Date
Wednesday - 20/04/08:
11 Days Before Due Date
Wednesday - 20/04/08:
11 Days Before Due Date
dat <- read.csv("data/Contraction_Clean.csv", stringsAsFactors = F)
dat$posix <- as.POSIXct(dat$posix)
head(dat)
## posix duration frequency ## 1 2020-04-08 10:33:00 72 420 ## 2 2020-04-08 10:40:00 28 420 ## 3 2020-04-08 10:47:00 57 480 ## 4 2020-04-08 10:55:00 60 1260 ## 5 2020-04-08 11:16:00 88 660 ## 6 2020-04-08 11:27:00 86 1620
dat[11:15,]
## posix duration frequency ## 11 2020-04-08 12:56:00 54 720 ## 12 2020-04-08 13:09:00 69 720 ## 13 2020-04-08 13:22:00 60 600 ## 14 2020-04-08 13:33:00 26 840 ## 15 2020-04-08 13:47:00 76 240
Wednesday - 20/04/08:
13-1-1
mean.duration <- mean(dat$duration[1:15]) mean.frequency <- mean(dat$frequency[1:15])
The mean duration is 66.27 seconds.
The mean frequency is 764 seconds.
Wednesday - 20/04/08:
11 Days Before Due Date
Plot
dat.plot <- dat[1:15,]
plot(x = dat.plot$posix, y = dat.plot$frequency,
ylim = c(min(dat.plot$duration, dat.plot$frequency),
max(dat.plot$duration, dat.plot$frequency)),
xlab = "Date/Time", ylab = "Length (s)", main = "Labor Frequency/Duration",
col = rep("#DF536B", nrow(dat.plot)),
pch = rep(6, nrow(dat.plot)))
points(x = dat.plot$posix, y = dat.plot$duration,
col = rep("#61D04F", nrow(dat.plot)),
pch = rep(2, nrow(dat.plot)))
## Wednesday - 20/04/08:
11 Days Before Due Date {.smaller}
Baby will come this weekend.
Thursday - 20/04/09:
10 Days Before Due Date
dat[16:20,]
## posix duration frequency ## 16 2020-04-09 08:00:00 75 300 ## 17 2020-04-09 08:07:30 36 450 ## 18 2020-04-09 08:13:45 83 375 ## 19 2020-04-09 08:20:00 101 375 ## 20 2020-04-09 08:34:30 78 870
dat[36:40,]
## posix duration frequency ## 36 2020-04-09 10:33:00 91 840 ## 37 2020-04-09 10:37:00 74 240 ## 38 2020-04-09 10:44:00 137 420 ## 39 2020-04-09 10:50:30 61 390 ## 40 2020-04-09 10:57:15 88 405
dat.plot <- dat[1:40,]
Thursday - 20/04/09:
7-1-1 7-2-1
mean.duration <- mean(dat$duration[16:40]) mean.frequency <- mean(dat$frequency[16:40])
The mean duration is 98.56 seconds.
The mean frequency is 437.4 seconds.
Thursday - 20/04/09:
11 Days Before Due Date
lm
mod.duration <- lm(duration~posix, data = dat.plot) mod.duration
## ## Call: ## lm(formula = duration ~ posix, data = dat.plot) ## ## Coefficients: ## (Intercept) posix ## -6.715e+05 4.233e-04
mod.frequency <- lm(frequency~posix, data = dat.plot) mod.frequency
## ## Call: ## lm(formula = frequency ~ posix, data = dat.plot) ## ## Coefficients: ## (Intercept) posix ## 6.767e+06 -4.266e-03
Plot
plot(x = dat.plot$posix, y = dat.plot$frequency,
ylim = c(min(dat.plot$duration, dat.plot$frequency),
max(dat.plot$duration, dat.plot$frequency)),
xlab = "Date/Time", ylab = "Length (s)", main = "Labor Frequency/Duration",
col = "#DF536B", pch = 6)
points(x = dat.plot$posix, y = dat.plot$duration,
col = "#61D04F", pch = 2)
abline(a = mod.frequency$coefficients[1], b = mod.frequency$coefficients[2],
col = "#DF536B")
abline(a = mod.duration$coefficients[1], b = mod.duration$coefficients[2],
col = "#61D04F")
Thursday - 20/04/09:
11 Days Before Due Date
Thursday - 20/04/09: Estimated 3-2-1
seq.posix <- seq(dat.plot$posix[nrow(dat.plot)], by = "sec", length.out = 86400*3)
freq.diff.311 <- 180 - predict(mod.frequency,
newdata = data.frame(posix = seq.posix))
time.311 <- seq.posix[which.min(abs(freq.diff.311))]
time.311
## [1] "2020-04-10 02:13:31 CDT"
duration.311 <- predict(mod.duration, newdata = data.frame(posix = time.311)) duration.311
## 1 ## 124.151
Thursday - 20/04/09:
10 Days Before Due Date
dat[41:46,]
## posix duration frequency ## 41 2020-04-09 16:15:00 134 300 ## 42 2020-04-09 16:19:15 142 255 ## 43 2020-04-09 16:25:30 90 375 ## 44 2020-04-09 16:31:30 123 360 ## 45 2020-04-09 16:36:45 104 315 ## 46 2020-04-09 16:48:45 139 720
tail(dat)
## posix duration frequency ## 51 2020-04-09 17:18:25 146 360 ## 52 2020-04-09 17:23:40 116 315 ## 53 2020-04-09 17:29:10 129 330 ## 54 2020-04-09 17:34:10 116 300 ## 55 2020-04-09 17:43:10 87 540 ## 56 2020-04-09 17:48:10 80 300
dat.plot <- dat
Thursday - 20/04/09:
6-2-1
mean.duration <- mean(dat$duration[41:nrow(dat)]) mean.frequency <- mean(dat$frequency[41:nrow(dat)])
The mean duration is 121 seconds.
The mean frequency is 368.1375 seconds.
Thursday - 20/04/09:
10 Days Before Due Date
Thursday - 20/04/09:
Estimated 3-1-1 3-2-1
freq.diff.311 <- 180 - predict(mod.frequency,
newdata = data.frame(posix = seq.posix))
time.311 <- seq.posix[which.min(abs(freq.diff.311))]
time.311
## [1] "2020-04-10 04:52:53 CDT"
duration.311 <- predict(mod.duration, newdata = data.frame(posix = time.311)) duration.311
## 1 ## 137.3447
Friday - 20/04/10:
4:30AM - Water Breaks!
Active Labor!!!
Birthing humans is hard.
It’s harder in a pandemic.
Birth plan changed at lockdown, and then 3 more times in the last few days.
Pro-tip: If you’re going to give birth on a Friday, do it before shift change at 5pm.
Joaquin!
